Systems and methods for assessing needs

ABSTRACT

Systems and methods for assessing the needs of customers using predictive modeling techniques are disclosed in which a server receives, from a computing device, a request to generate a recommendation for a first user, the server further receiving a set of attributes of the first user; identifies at least one missing attribute for the first user and an existing user profile corresponding to a second user having the set of attributes; executes an artificial intelligence model trained based on personas corresponding to a set of existing users, wherein the artificial intelligence model estimates the at least one missing attribute of the first user; updates a user profile of the first user using the estimated at least one missing attribute generated by the artificial intelligence model; generates the recommendation based on the updated user profile; and transmits the recommendation to be displayed on the graphical user interface.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 15/287,503, filed on Oct. 6, 2016, now issued as U.S. Pat. No.10,546,340, which claims priority to U.S. Provisional Patent ApplicationSer. No. 62/238,020, filed on Oct. 6, 2015, each of which is herebyincorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

The present disclosure relates in general to insurance products, andmore particularly, to predictive modeling techniques used to recommendproducts.

BACKGROUND

The completion of insurance forms, such as an insurance application, canbe time-consuming for customers, agents, or any other person involved inthe insurance business. Completing these forms often requires thatcustomers provide numerous items of information. The conventionalprocesses of obtaining the necessary information from a potentialcustomer regarding an insurance product application are severelyoutdated and oftentimes implemented as manual processes that areextremely cumbersome. For example, insurance agents often have tomanually key in the bulk of business data, such as the business name,address, driver names, driver addresses, and Vehicle IdentificationNumber (VIN), and then provide insurance product information to thecustomer to obtain a sale.

These manual processes are extremely time consuming and prone to usererror. Because of this time-consuming task of data-entry, the customermay not always finish the process and often leave the applicationincomplete.

For the aforementioned reasons, there is a need for an efficient methodand/or system that improves or makes the customer experience faster tosell more insurance services or products.

SUMMARY

For the aforementioned reasons, there is a need for a more efficient andfaster system and method for processing large data sets which wouldallow institutions to profile customers in a more efficient manner thanpossible with human-intervention data-driven analysis. There is a needfor a network and computer-specific solution to reduce the level ofdata-entry efforts required from the customers. These features allowperforming large work such as time-consuming analysis, data-entry tasks,filling out customer profiles, and generating insurance recommendations,in a more efficient manner than other approaches including manual workperformed by humans or other conventional methods.

An embodiment for determining customer insurance needs comprises amethod which comprises receiving inputted user data stored in a firstdatabase. The method comprises generating an instruction to a seconddatabase to transmit additional data associated with the user. Themethod comprises upon transmitting the first instruction to the seconddatabase, receiving and storing additional user data in the firstdatabase. The method comprises generating a customer profile based onthe inputted user data and the additional user data. The methodcomprises determining missing data in the customer profile. The methodcomprises determining a set of attributes of the user based on thecustomer profile. The method comprises identifying another profile withthe same set of attributes. The method comprises generating estimateddata for the missing data based on the other profile having the same setof attributes. The method comprises generating a second instruction toupdate the customer profile to include the estimated data. The methodcomprises determining a financial priority for the updated customerprofile and generating insurance recommendations for the updatedcustomer profile using an insurance needs algorithm and based upon thefinancial priority.

Another embodiment for identifying insurance recommendations comprises acomputer system having a first database, a second database, and aserver. The server may be configured to receive inputted user datastored in a first database. The server may be configured to generate aninstruction to the second database to transmit additional dataassociated with the user. The server may be configured to receive andstore additional user data in the first database, upon transmitting thefirst instruction to the second database. The server may be configuredto generate a customer profile based on the inputted user data and theadditional user data. The server may be configured to determine missingdata in the customer profile. The server may be configured to determinea set of attributes of the user based on the customer profile. Theserver may be configured to identify another profile with the same setof attributes. The server may be configured to generate estimated datafor the missing data based on the other profile having the same set ofattributes. The server may be configured to generate a secondinstruction to update the customer profile to include the estimateddata. The server may be configured to determine a financial priority forthe updated customer profile and generate an insurance recommendationfor the updated customer profile using an insurance needs algorithm andbased upon the financial priority.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure herein is described with reference to embodimentsillustrated in the drawings, which form a part herein. Other embodimentsmay be used and/or other changes may be made without departing from thespirit or scope of the present disclosure. The illustrative embodimentsdescribed are not meant to be limiting of the subject matter presentedherein.

FIG. 1 is a block diagram illustrating a system architecture of aninsurance needs system, according to an embodiment.

FIG. 2 is a block diagram illustrating an example computing device orserver in which one or more embodiments of the present disclosure mayoperate, according to an embodiment.

FIG. 3 is a block diagram illustrating a sub-system of the insuranceneeds system of FIG. 1 pertaining to a profile analytics engine,according to an embodiment.

FIG. 4 is a block diagram illustrating another sub-system of theinsurance needs system of FIG. 1 pertaining to an insurance needsengine, according to an embodiment.

FIG. 5 is a flow diagram describing a method for assessing insuranceneeds using predictive analytics, according to an embodiment.

FIG. 6 is a flow diagram describing a method for recommending aninsurance product for a customer, according to an embodiment.

FIG. 7 is a flow diagram describing a method for evaluating a predictivemodeling technique, according to an embodiment.

DETAILED DESCRIPTION

The present disclosure herein is described in detail with reference toembodiments illustrated in the drawings, which form a part here. Otherembodiments may be used and/or other changes may be made withoutdeparting from the spirit or scope of the present disclosure. Theillustrative embodiments described in the detailed description are notmeant to be limiting of the subject matter presented here.

As used here, the following terms may have the following definitions:

“Additional Customer Information” refers to one or more attributesassociated with a customer provided by external databases to aninsurance needs system.

“Basic Customer Information” refers to one or more attributes associatedwith a customer provided by users to an insurance needs system viaclient computing device.

“Basic Profile” refers to a basic description of a customer that caninclude basic customer information and/or supplemental customerinformation provided by users, as well as, additional customerinformation received from external databases.

“Estimated customer information” refers to one or more attributesassociated with a customer not provided by the customer or readilyavailable from external databases and derived by estimating theinsurance needs associated with the customer and other customerattributes using predictive modeling techniques.

“Full Profile” refers to the full description of a customer thatincludes: basic customer information and supplemental customerinformation provided by one or more users, additional customerinformation provided by one or more external databases, and theestimated customer information predicted by an insurance needs system.

“Persona” refers to non-personally identifiable information derived fromthe full profile of one or more customers and/or from an initial set ofpreviously generated personas, such as, for example personas obtainedfrom a third-party (e.g., purchased from a third-party persona vendor).The personas are used as a training data set for applying predictivemodeling techniques to discover potentially predictive relationships.

“Supplemental Customer Information” refers to one or more attributesassociated with a customer provided by one or more users to an insuranceneeds system via one or more client computing devices to modify and/orrefine one or more fields of the pre-populated full profile associatedwith the customer.

The present disclosure describes systems and methods for assessinginsurance needs of customers using predictive modeling techniques. Thesystem describes an insurance needs system that uses basic customerinformation and additional customer information to generate a basicprofile for the customer. The insurance needs system can additionallyanalyze the basic profile associated with the customer by employingpredictive modeling techniques to generate estimated customerinformation. The systems and methods then pre-populate one or morefields of the full profile associated with the customer based on the useof said basic customer information and additional customer informationcontained within the basic profile, as well as with said estimatedcustomer information. The systems and methods then analyze the fullprofile associated with the customer to generate one or moreinsurance-product recommendation including insurance product(s) thatmeet the needs of the customer and fulfill one or more proposed futuregoals of the customer.

The disclosed system architecture includes one or more components, suchas, an internal database and an external database, one or morecommunication networks, a profile analytics engine, an insurance needsengine, a user interface and one or more client computing devices.

In some embodiments, a user creates a new account for a customer andprovides basic customer information by interacting with a user interfacerunning on a client computing device and coupled to the profileanalytics engine via communication networks. The profile analyticsengine receives and processes said basic customer information andreceives additional customer information from one or more externaldatabases. The profile analytics engine generates a basic profile forthe customer based on the customer information and the additionalcustomer information. The profile analytics engine then stores saidbasic profile at the internal database. The profile analytics enginethen analyzes the basic profile associated with the customer to generateestimated customer information using predictive modeling techniques. Theprofile analytics engine then employs said estimated customerinformation to pre-populate one or more fields of the full profileassociated the customer. The profile analytics engine then displays saidfull profile to the user and the user can then validate the estimatedcustomer information and provide supplemental customer information torefine/modify one or more pre-populated fields of the full profileassociated with the customer, if desired. The insurance needs enginethen employs the full profile associated with the customer to identifythe insurance needs associated with customer and generate one or moreinsurance-product recommendations that satisfy the insurance needs andfuture goals of the customer. The insurance needs engine then displayssaid insurance-product recommendations to the user via the userinterface, and the user can then provide additional insurance-needs datato the insurance needs engine via the user interface to modify theinsurance needs identified by the insurance needs engine, if desired.The insurance needs of the customer can be directed towards determiningone or more insurance products, such as, for example health insurance,whole life insurance, term life insurance, universal life insurance,variable universal life (VUL), disability income insurance, annuities,long term care, and the like. The insurance needs engine can thengenerate a modified insurance-product recommendation based on theprovided additional insurance-needs data and the full profile associatedwith the customer.

In some embodiments, a sub-system of the insurance needs system includesa profile analytics engine that further includes a data processingmodule and a prediction module. In some embodiments, a user creates anew account for a customer and provides basic customer information byinteracting with a user interface running on a client computing devicethat is coupled to the profile analytics engine. The data processingmodule receives and processes basic customer information from the userand/or external databases to create a basic profile for the customer andstore it in the internal database. In some embodiments, the dataprediction module analyzes the customer's basic profile and usespredictive modeling techniques to estimate the insurance needs of thecustomer as well as other customer attributes. In these embodiments, thedata prediction module:

-   -   uses the estimated customer information to pre-populate the full        profile associated with the customer;    -   displays the customer's full profile to the user for validation        purposes (the user can validate and/or refine one or more fields        of the customer's full profile when needed);    -   uses the validated/adjusted pre-populated fields to better        predict the remaining missing fields of the customer's full        profile;    -   evaluates the performance of the predictive modeling technique        being applied to estimate the customer's insurance needs and        other customer attributes; and    -   selects/applies the performance evaluation technique based on        the amount and type of basic information known about the        customer, and on the predictive model being applied.

In some embodiments, a sub-system of the insurance needs system includesan insurance needs engine that further includes an insurancerecommendation module. In some embodiments, the insurance recommendationmodule operating within the insurance needs engine receives the fullprofile associated with the customer from the internal database. Theinsurance recommendation module then analyses said full profile toidentify the insurance needs of the customer employing insurance tools(e.g., risk and/or underwriting analysis), data mining and other dataprocessing. The insurance recommendation module then generates one ormore insurance-product recommendations based on said insurance needs ofthe customers. The insurance recommendation module then stores saidinsurance-product recommendation at the internal database and theinsurance needs engine displays said insurance-product recommendation tothe user via client computing devices. The user can then provideadditional insurance-needs data to the insurance recommendation moduleto adjust/modify the insurance needs of the customer, if desired.

In some embodiments, a computer-implemented method for assessing theinsurance needs of customers using predictive modeling techniquesincludes the following steps:

-   -   creating a basic profile for a customer based on basic customer        information provided by the user;    -   requesting additional customer information from one or more        external databases to update the basic profile associated with        the customer;    -   running a predictive modeling technique to generate estimated        customer information;    -   pre-populating the full profile associated with the customer        with said estimated customer information; displaying the full        profile associated with the customer for validation purposes;    -   refining the basic profile associated with the customer, if        desired; and    -   recommending one or more insurance product for the customer.

In some embodiments, a computer-implemented method for recommending oneor more insurance products for a customer includes the following steps:analyzing the full profile of the customer to determine the insuranceneeds of customer; generating and storing the insurance-productrecommendation about one or more insurance products for the customer;and displaying said insurance-product recommendation to the customerthrough a user interface running on a client computing device.

Systems and methods for assessing the insurance needs of customer usingpredictive analytics reduce the level of data-entry effort required byusers (e.g., insurance/financial agents or customers) to generate a fullprofile that can be analyzed to recommend one or more insuranceproducts. In addition, these systems and methods can provide customerswith insurance products that best fit the needs of the customer andallow the customer to meet one or more defined future goals whileensuring their financial stability as well as the welfare of the familyof the customer.

Numerous other aspects, features and benefits of the present disclosuremay be made apparent from the following detailed description takentogether with the figures.

In some embodiments, an insurance company can implement a statisticalanalysis that deals with different aspects of an insurance transaction,such as, for example, extracting information from any data source, usingpredictive future trends and behavior patterns, among others.

In some embodiments, the predictive analytics can be used in differentareas, such as insurance and/or financial services, which can rely oncapturing relationships between explanatory variables and the predictedvariables from past occurrences, as well as, exploiting these variablesto determine future outcomes. The predictive analytics can beadditionally used in insurance industry for sales and marketingpurposes, predicting customer behaviors and needs, anticipating customerreactions to promotions, and reducing acquisition cost (direct email,discount program, etc.).

Reference will now be made to the exemplary embodiments illustrated inthe drawings, and specific language will be used here to describe thesame. It will nevertheless be understood that no limitation of the scopeof the invention is thereby intended. Alterations and furthermodifications of the inventive features illustrated here, and additionalapplications of the principles of the inventions as illustrated here,which would occur to one skilled in the relevant art and havingpossession of this disclosure, are to be considered within the scope ofthe invention.

The present disclosure describes systems and methods for assessinginsurance needs using predictive modeling techniques. In someembodiments, the system employs basic customer information andadditional customer information to generate a basic profile for thecustomer. The system then analyzes the basic profile associated with thecustomer using predictive modeling techniques to generate estimatedcustomer information. The system then pre-populates one or more fieldsof the full profile associated with the customer based on said estimatedcustomer information. The system then analyzes the full profileassociated with the customer to generate one or more insurance productsfor the customer including the insurance reallocation recommendations tomeet the insurance needs of the customer and fulfill one or moreproposed future goals.

The term predictive modeling technique as used herein includes any rulesor techniques using statistical approaches for using a computer todetermine a probable or most likely one of a set of possible outputs orvalues, based on input data. The Predictive modeling techniques aretypically created by applying algorithms to sets of data having knownresults, identified as training data, and then testing resultingpredictive modeling techniques against a set of similar data. Thepredictive modeling techniques can be understood as heuristic techniquesfor creating a statistical model of customer behavior based on inputdata. Examples of predictive models include K-Nearest-neighborAlgorithms, non-negative matrix factorization, rotation forest, randomforest technique, Naïve Bayesian Models, Bayesian Network Models,Support Vector Machines, hybrid collaborative filtering algorithmapproaches, and the like.

System Architecture

FIG. 1 is a block diagram illustrating a system architecture of aninsurance needs system, according to an embodiment. In FIG. 1, insuranceneeds system 100 includes components such as internal database 102,external database 104, profile analytics engine 106, insurance needsengine 108, user interface 110, client computing devices 112, 114, and116, and communication networks 118, 120, and 122. Insurance needssystem 100 additionally includes one or more servers (not shown inFIG. 1) having the necessary hardware and software/firmware to implementany of the aforementioned system components that require implementationvia such necessary hardware and software/firmware, among others. Itshould be understood that insurance needs system 100 can include lesscomponents, more components, or different components depending on thedesired analysis goals.

In FIG. 1, internal database 102 is coupled to and in bidirectionalcommunication with profile analytics engine 106 and insurance needsengine 108 through communication network 120. External database 104 iscoupled to and in bidirectional communication with profile analyticsengine 106 through communication network 118. Profile analytics engine106 and insurance needs engine 108 are each coupled to and inbidirectional communication with user interface 110 throughcommunication network 122. User interface 110 is coupled to and inbidirectional communication with one or more client computing devices112, 114, and 116.

In some embodiments, internal database 102 is implemented as a set ofcomputer instruction executed by central processing units that runcomputer executable program instructions or related algorithms on aserver, configured as a relational database and designed to fetch, indexand store customer data, and provide said stored customer data toauthenticated requesters. In these embodiments, internal database 102 isconfigured to receive and store basic profiles and full profilesprovided from profile analytics engine 106, and to receive and storeinsurance product information associated with one or more insuranceproducts and provided by insurance needs engine 108. Further to theseembodiments, internal database 102 is configured to receive requests forbasic profiles and full profiles from profile analytics engine 106, andto provide said stored basic profiles and full profiles to profileanalytics engine 106. In these embodiments, internal database 102 isfurther configured to receive requests for information regardinginsurance products from insurance needs engine 108 and to provide saidstored insurance product information to insurance needs engine 108. Inother embodiments, internal database 102 includes an initial set ofpreviously generated personas, such as, for example personas obtainedfrom a third-party (e.g., purchased from a third-party persona vendor).In these embodiments, internal database 102 is configured to receiverequests for the initial set of previously generated personas fromprofile analytics engine 106, and to provide said stored initial set ofpreviously generated personas to profile analytics engine 106 whenrequested.

In some embodiments, code implementing internal database 102 can behoused locally or remotely, for instance, in a secure, scalablefacility. In other embodiments, for security and reliability, redundancymay be employed to protect the data stored within internal database 102.In an example, internal database 102 is configured as a databaseimplementing MySQL, PostgreSQL, SQLite, Microsoft SQL Server, MicrosoftAccess, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base, FileMakerPro and/or any other type of database that organize collections of data.

In some embodiments, external database 104 is implemented as a set ofcomputer instructions executed by central processing units that runcomputer executable program instructions or related algorithms on aserver, configured as a relational database and designed to provideadditional customer information derived from ties formed betweenindividuals and/or organizations using, for example, the Internet.Example of external databases 104 include the World Wide Web, externalsocial networks, external consulting firms, third party providers,external project sources and the like. In these embodiments, externaldatabase 104 provides additional customer information, as well as otherstored customer data and/or customer files to one or more softwaremodules operating within insurance needs engine 108.

In some embodiments, profile analytics engine 106 is a collection of oneor more software modules configured to:

-   -   receive basic customer information, additional customer        information, and store customer data and/or customer files;    -   generate a basic profile associated with the customer based on        said basic customer information, additional customer        information, and stored costumer data and/or customer files;    -   generate estimated customer information based on said basic        profile; and    -   generate a full profile associated with the customer based on        said basic profile and said estimated customer information.        In these embodiments, profile analytics engine 106 receives        basic customer information from one or more user interfaces 110        associated with one or more client computing devices 112, 114,        and 116; receives additional customer information from external        databases 104, and generates a basic profile for the customer        based on the received basic customer information and additional        customer information. Further to these embodiments, additional        customer information can be generated by profile analytics        engine 106 based on unstructured data received from external        databases 104. Examples of unstructured data include text,        images, social networking relationships, financial statements,        insurance information, demographic information, health        information and the like. In these embodiments, profile        analytics engine 106 stores the generated basic profile at        internal database 102. In some embodiments, a basic profile        includes one or more attributes associated with a customer, such        as, for example: age, gender, ethnicity, place of residence,        marital status, number and identity of dependent persons (e.g.,        children and dependent adults), expenditures, savings,        approximate market value of assets and their composition,        education, professional status, future goals (e.g., new house,        new car, investments, new additions to the family, education,        etc.), and the like. The insurance needs of the customer can be        directed towards determining one or more insurance products such        as, for example, health insurance, whole life insurance, term        life insurance, universal life insurance, variable universal        life (VUL), disability income insurance, annuities, long term        care, and the like.

In other embodiments, profile analytics engine 106 receives a basicprofile associated with a customer from internal database 102. In theseembodiments, profile analytics engine 106 additionally receives aplurality of personas from internal database 102 where said personasincludes an initial set of previously generated personas, as well as,non-personally identifiable information derived from the full profile ofcustomers stored within internal database 102. Further to theseembodiments, profile analytics engine 106 generates estimated customerinformation using predictive modeling techniques and employing thepersonas as training data. Examples of predictive modeling techniquesinclude K-Nearest-neighbor Algorithms, non-negative matrixfactorization, rotation forest, random forest technique, Naïve BayesianModels, Bayesian Network Models, Support Vector Machines, hybridcollaborative filtering algorithm approaches, and the like. In theseembodiments, profile analytics engine 106 employs the estimated customerinformation to pre-populate one or more missing fields of the fullprofile associated with the customer, and stores said full profile atinternal database 102. Further to these embodiments, the full profileassociated with the customer includes, for example, demographic data,health historical data, financial data, insurance information,activities information, future goals, and the like. In some embodiments,the greater the number of attributes the user provides within the basiccustomer information to generate the basic profile of the customer, themore accurately the system can determine the estimated customerinformation included within a full profile associated with the customer.Profile analytics engine 106 will be further detailed in FIG. 3, below.

In some embodiments, insurance needs engine 108 is a collection of oneor more software modules configured to receive a full profile associatedwith a customer from internal database 102, additionally receiveinsurance-needs data from client computing devices 112, 114, and 116and/or database 102, determine one or more insurance products associatedwith the customer based on said full profile and said insurance-needsdata, and provide said insurance product(s) to internal database 102and/or user interface 110. In these embodiments, insurance needs engine108 receives the full profile associated with a customer from internaldatabase 102 and generates one or more insurance products based on saidfull profile. In other embodiments, insurance needs engine 108additionally receives insurance-needs data from users interacting withclient computing devices 112, 114, and 116 via user interface 110 andgenerates one or more insurance product for the customer based on thereceived full profile and the received insurance-needs data. In someembodiments, the insurance product information provides customerinstructions directed towards prioritizing the establishment of one ormore insurance products, ensuring financial security with properinsurance product(s) allowing the customer to protect and guaranteehis/her welfare, as well as fulfilling one or more proposed future goals(e.g., new house, new car, investments, new additions to the family,education), among others. In these embodiments, the insurance productinformation includes a recommendation of one or more insuranceproduct(s) that meets the customer needs. Examples of insurance productsinclude health insurance, whole life insurance, term life insurance,universal life insurance, variable universal life (VUL), disabilityincome insurance, annuities, long term care, and the like.

In some embodiments, insurance needs engine 108 provides the insuranceproduct information to internal database 102 for storage. In otherembodiments, insurance needs engine 108 provides the insurance productinformation to user interface 110 for displaying to the user. Insuranceneeds engine 108 will be further detailed in FIG. 4, below.

In some embodiments, each software module within profile analyticsengine 106 and insurance needs engine 108 is implemented as a coderunning on a processing unit configured for running related algorithmsor computer executable program instructions that are executed by aserver, a single computer, or multiple computers within a distributedconfiguration. In these embodiments, the processing unit is configuredto interact with one or more software modules of the same or differenttypes operating within one or more of profile analytics engine 106and/or insurance needs engine 108. Further to these embodiments, eachprocessing unit includes a processor with computer-readable medium, suchas, for example, a random access memory (RAM), coupled to the processor.Examples of processor types include a microprocessor, an applicationspecific integrated circuit (ASIC), and a field programmable objectarray (FPOA), among others. Examples of firmware and/or hardware andassociated software enabling functionality of the aforementioned systemcomponents will be further described in FIG. 2, below.

In FIG. 1, user interface 110 is configured to interact with one or moreusers to receive basic customer information and/or insurance-needs data,and to distribute said basic customer information and/or insurance-needsdata to other components within insurance needs system 100. In someembodiments, user interface 110 is additionally configured to receivethe full profile associated with a customer from profile analyticsengine 106, and/or receive one or more insurance products associatedwith the customer from insurance needs engine 108, and display said fullprofile and/or insurance product(s) to one or more users. In theseembodiments, user interface 110 is configured to receive basic customerinformation and insurance-needs data from one or more users via clientcomputing devices 112,114, and 116, to provide said basic customerinformation to profile analytics engine 106, and to provide saidinsurance-needs data to insurance needs engine 108. Further to theseembodiments, user interface 110 is configured to receive a full profileassociated with a customer from profile analytics engine 106 viacommunication network 122 and display said full profile to one or moreusers via client computing devices 112, 114, and 116. In someembodiments, users interacting with user interface 110 can then validatethe estimated customer information predicted by profile analytics engine106. In these embodiments, user interface 110 is configured to receivesupplemental customer information from said users and provide saidsupplemental customer information to profile analytics engine 106. Inother embodiments, user interface 110 is configured to receive theinsurance product(s) associated with a customer from insurance needsengine 108 via communication network 122 and display said insuranceproduct(s) to one or more users via client computing devices 112, 114,and 116. In these embodiments, user interface 110 is configured toreceive insurance-needs data from one or more users and provide saidinsurance product(s) data to insurance needs engine 108. In an example,users interacting with client computing devices 112, 114, and 116include financial agents, insurance agents, customers, and the like.

In other embodiments, user interface 110 is configured to allow a userto create an account associated with a customer. In these embodiments,the user interacts with user interface 110 that is running on clientcomputing devices 112, 114, and 116 via an input device, such as, forexample a touch screen, a mouse, a keyboard and/or a keypad working inconcert with a display, and the like. In an example, the user can be afinancial agent, insurance agent, or a customer who wants to obtaininformation for one or more insurance products that best fit thecustomer needs. In this example, the user interacts with user interface110 to provide basic customer information including one or moreattributes, such as, for example: gender, age, marital status, number ofchildren/dependents, profession, owning an insurance product/retirementplan, and the like. Further to this example, the customer canadditionally establish a plurality of future goals, such as, forexample: “retire within the next 3 year.”

In some embodiments, user interface 110 is implemented as a set ofcomputer instructions executed on client computing devices 112, 114, and116 by central processing units that run computer executable programinstructions or related algorithms. In some embodiments, user interface110 can be implemented as a browser or an application running on clientcomputing devices 112, 114, and 116.

In some embodiments, client computing devices 112, 114, and 116 includea set of software instructions in combination with hardware equipment orfirmware to allow users to interact with various components of profileanalytics engine 106 and insurance needs engine 108. Examples of clientcomputing devices 112, 114, and 116 include a smartphone, a desktopcomputer, a laptop computer, a tablet, a PDA and/or any other type ofprocessor-controlled device that can receive, process and/or transmitdigital data.

In some embodiments, communication networks 118, 120, and 122 areimplemented as one or more networks connecting the different componentswithin insurance needs system 100 and allowing said components tointeract with one another. Communication networks 118, 120, and 122include hardware and associated software/firmware for allowingcommunication between one or more components of insurance needs system100. Examples of communication networks 118, 120, and 122 includeintranets, local area networks (LANs), the cloud, virtual privatenetworks (VPNs), wide area networks (WANs) and the internet, amongothers.

In operation, a user creates a new account for a customer and providesbasic customer information by interacting with user interface 110running on client computing devices 112, 114, and 116 and coupled toprofile analytics engine 106 via communication network 122. Profileanalytics engine 106 receives and processes said basic customerinformation, and receives additional customer information from one ormore external databases 104. Profile analytics engine 106 generates abasic profile for the customer based on the basic customer informationand the additional customer information. Profile analytics engine 106then stores said basic profile at internal database 102. Profileanalytics engine 106 then analyzes the basic profile associated with thecustomer to generate estimated customer information using predictivemodeling techniques. Profile analytics engine 106 then employs saidestimated customer information to pre-populate one or more fields of thefull profile associated with the customer. Profile analytics engine 106then displays said full profile to the user and the user can thenvalidate the estimated customer information and provides supplementalcustomer information to refine/modify one or more fields of thepre-populated full profile associated with the customer, if desired.Insurance needs engine 108 then employs the full profile associated withthe customer to identify the insurance needs of the customer andgenerate one or more insurance products that best fit the customer needsand future goals of the customer. Insurance needs engine 108 thendisplays said insurance product(s) to the user via user interface 110.The user can then provide additional insurance-needs data to insuranceneeds engine 108 via user interface 110. When a user provides additionalinsurance-needs data, insurance needs engine 108 modifies the insuranceproduct(s) information based on the additional insurance-needs data. Insome embodiments, insurance needs engine 108 can generate one or moreinsurance product(s) based on the provided insurance-needs data and/orthe additional insurance-needs data (if available), and the full profileassociated with the customer.

FIG. 2 is a block diagram illustrating an exemplary computing device orserver in which one or more embodiments of the present disclosure mayoperate, according to an embodiment. In FIG. 2 computing device 200includes example components of client computing devices that may operatewithin insurance needs system 100 of FIG. 1, execute modules describedin FIGS. 3 and 4, or execute code implementing concepts/ideas containedin FIGS. 5 and 6, according to an exemplary embodiment. In oneembodiment, computing device 200 includes bus 202, input/output (I/O)device 204, communication interface 206, memory 208, storage device 210and central processing unit 212 (CPU). In some embodiments, computingdevice 200 includes additional, fewer, different, or differentlyarranged components than those illustrated in FIG. 2.

In FIG. 2, bus 202 is in physical communication with I/O device 204,communication interface 206, memory 208, storage device 210 and centralprocessing unit 212. In some embodiments, bus 202 includes a path thatpermits components within computing device 200 to communicate with eachother. Examples of I/O device 204 include any peripheral and/or othermechanisms that enable a user to input information to computing device200, such as, for example, a keyboard, a computer mouse, a track ball,other input buttons, touch screens, voice recognition devices, biometricmechanisms and the like. In these embodiments, (I/O) device 204additionally includes a mechanism that outputs information to the userof computing device 200, such as, for example, a display, a lightemitting diode (LED), a printer, a speaker and the like.

In FIG. 2, communication interface 206 is a device that enablescomputing device 200 to communicate with other computing devices and/orsystems through communication networks (not shown in FIG. 2), such as,for example, Wi-Fi cards, Ethernet and the like. In some embodiments,communication networks refer to any communication network betweencomputers that allows computing device 200 to exchange data, such as,for example, intranets, local area networks (LANs), virtual privatenetworks (VPNs), wide area networks (WANs), the internet and the like.Memory 208 is a device that stores software programs or data used incomputing device 200, such as, for example, a random access memory(RAM), a read-only memory (ROM), a flash memory and the like. In FIG. 2,storage device 210 is a device that stores and retrieves information,such as, for example, magnetic and/or optical recording medium,Ferro-electric RAM (F-RAM) hard disks, solid-state drives, floppy disks,optical discs and the like. In one embodiment, memory 208 and storagedevice 210 store information and instructions for execution by one ormore central processing units 212. Examples of central processing units212 include a microprocessor, an application specific integrated circuit(ASIC), a field programmable object array (FPOA) and the like. In thisembodiment, central processing unit 212 interprets and executesinstructions retrieved from memory 208 and storage device 210.

In some embodiments, computing device 200 can be implemented as part ofa server, client computing devices and the like. Examples ofimplementations of computing device 200 include servers, authorizedclient computing devices 112, 114, and 116, smartphones, desktopcomputers, laptop computers, tablet computers, PDAs and other types ofprocessor-controlled devices that can receive, process, and/or transmitdigital data. In an example, computing device 200 performs certainoperations that are required for the proper operation of insurance needssystem 100. Computing devices 200 perform these operations because ofcentral processing unit 212 executing software instructions containedwithin a computer-readable medium, such as within memory 208.

In one embodiment, the software instructions of the system are read intomemory 208 from another memory location, such as from storage device210, or from another computing device (e.g., client computing devices112, 114, and 116) via communication interface 206. In this embodiment,the software instructions contained within memory 208 instructs centralprocessing unit 212 to perform processes that will be further describedin FIGS. 3-6, below. Alternatively, hardwired circuitry may be used inplace of or in combination with software instructions to implement theprocesses described herein. Thus, implementations described herein arenot limited to any specific combinations of hardware circuitry andsoftware.

FIG. 3 is a block diagram illustrating a sub-system of the insuranceneeds system of FIG. 1 pertaining to a profile analytics engine,according to an embodiment. In FIG. 3, sub-system 300 includes internaldatabase 302, external database 304, and profile analytics engine 306.Profile analytics engine 306 further includes data processing module 308and prediction module 310. It should be understood that sub-system 300can include fewer components, more components, or different componentsdepending on the desired analysis goals and that the components may bearranged differently than illustrated in sub-system 300 of FIG. 3.

In FIG. 3, profile analytics engine 306 is operatively coupled to and inbidirectional communication with internal database 302 and externaldatabase 304 through communication networks (not shown in FIG. 3). In anexample and referring to FIG. 1, profile analytics engine 306 functionsin a substantially similar manner to profile analytics engine 106 withininsurance needs system 100. Continuing the example, internal database302 and external database 304 function in a substantially similar mannerto internal database 102 and external database 104, respectively, withininsurance needs system 100.

In some embodiments, each software module within profile analyticsengine 306 includes a separate processing unit for running algorithms orcomputer-executable program instructions related to the operation of themethods described in FIGS. 5 and 6. The processing unit includes aprocessor with computer-readable media, such as, for example, a randomaccess memory (RAM) (not shown in FIG. 3), coupled to the processor.Examples of processor types include a microprocessor, an applicationspecific integrated circuit (ASIC), and a field programmable objectarray (FPOA), among others.

In some embodiments, profile analytics engine 306 is configured togenerate one or more fields of the pre-populated full profile associatedwith the customer based on basic customer information, additionalcustomer information, and estimated customer information; provide thegenerated full profile to a user interface for displaying to a userassociated with the customer; receive supplemental customer informationfrom the user via the user interface; and to validate and/orrefine/modify one or more fields of the full profile associated with thecustomer based on the supplemental customer information. Estimatedcustomer information is customer information not provided by thecustomer or readily available from external databases and derived bydetermining the financial behavior of the customer and other customerattributes using predictive modeling techniques. Further to theseembodiments, refining one or more fields of the pre-populated fullprofile includes modifying one or more fields with the supplementalcustomer information. In other embodiments, refining one or more fieldsof the pre-populated full profile includes modifying one or more fieldswith the supplemental customer information, as well as, modifying one ormore fields with updated estimated customer information. In theseembodiments, updated estimated customer information is estimatedcustomer information that has been modified based on updated basic,additional, and/or supplemental customer information and usingpredictive modeling techniques.

In some embodiments, data processing module 308 is a software moduleconfigured to receive and process basic customer information andadditional customer information and generate a basic profile associatedwith the customer based on said basic customer information andadditional customer information. In these embodiments, a user creates anaccount associated with a customer using profile analytics engine 306and provides basic customer information by interacting with a userinterface, running on a client computing device and coupled to profileanalytics engine 306. Further to these embodiments, data processingmodule 308 receives and processes said basic customer information,receives additional customer information from one or more externaldatabases 304, and generates a basic profile for the customer based onsaid basic customer information and said additional customerinformation. In these embodiments, data processing module 308 isadditionally configured to receive unstructured data from externaldatabases 304 and processes said unstructured data into additionalcustomer information, and/or receive supplemental customer informationfrom a user interacting with the user interface running on the clientcomputing device. In these embodiments, data processing module 308stores said basic profile at internal database 302. In otherembodiments, data processing module 308 receives requests for basicprofiles from prediction module 310 and provides said basic profiles toprediction module 310.

In an example, a user is a financial agent, insurance agent, or acustomer who wants to obtain the insurance product(s) that best fit thecustomer's needs. In this example, the user interacts with the userinterface, running on a client computing device and coupled to profileanalytics engine 306 to provide basic customer information. In thisexample, data processing module 308 employs said basic customerinformation provided by the user to request additional customerinformation from one or more external databases 304, and then generatesthe basic profile associated with the user.

In some embodiments, prediction module 310 is a software moduleincluding predictive models and is configured to receive the basicprofile associated with a customer, generate estimated customerinformation based on the basic profile associated with the customer, andadditionally generate a full profile associated with the customer basedon said basic profile and said estimated customer information. In theseembodiments, prediction module 310 receives the basic profile associatedwith the customer from internal database 302 or from data processingmodule 308. Further to these embodiments, data processing module 308additionally receives a plurality of personas from internal database 302where said personas include an initial set of previously generatedpersonas, as well as, non-personally identifiable information derivedfrom the full profile of customers stored within internal database 302.In these embodiments, data processing module 308 analyzes said basicprofile by applying a predictive modeling technique employing saidpersonas as training data to generate estimated customer information.Further to these embodiments, prediction module 310 analyzes said basicprofile by applying a predictive modeling technique employing thepersonas as training data to determine which specific personas mostclosely match said basic profile. In another embodiment, predictionmodule 310 analyzes said basic profile by applying a predictive modelingtechnique employing the personas as training data to determine whichportions of a plurality of personas most closely match associatedportions of said basic profile. Examples of predictive modelingtechniques include K-nearest-neighbor algorithms, non-negative matrixfactorization, rotation forest, random forest technique, naïve Bayesianmodels, Bayesian network models, support vector machines, hybridcollaborative filtering algorithm approaches, and the like. In someembodiments, the greater the number of attributes the user provides tosaid basic customer information that is employed by data processingmodule 308 to generate the basic profile of the customer, the moreaccurately the system can determine said estimated customer informationthat is included within the full profile associated with the customer.

In some embodiments, prediction module 310 is configured to determineand apply the predictive modeling technique that can more accuratelygenerate said estimated customer information based on the attribute typeand number of attributes included within the basic profile of thecustomer. In other embodiments, prediction module 310 is furtherconfigured to prioritize the attributes within basic customerinformation that profile analytics engine 306 requests from the user,and/or to assign optimal weights to each attribute or variable includedwithin the basic profile of the customer for the prediction process, ifdesired. In these embodiments, prediction module 310 assigns saidweights based on the type of attributes within the customer informationincluded in the basic profile of the customer, as well as, on thepredictive modeling technique being applied to generate said estimatedcustomer information. In this way, higher-prioritized attributes can beweighted more heavily when determining a user's insurance needs.

In other embodiments, prediction module 310 employs the estimatedcustomer information to pre-populate one or more missing fields of thefull profile associated with the customer, and then stores said fullprofile at internal database 302.

In an example, prediction module 310 is able to identify one or morepersonas that best match a 42-year-old married male individual fromCalifornia, employing K-nearest-neighbor algorithms. In this example,prediction module 310 employs conditional probability distributions andthe value of the attributes or variables associated with theK-nearest-neighbors or K closest personas to the customer, to generateestimated customer information, and then pre-populate one or moremissing fields of the full profile associated with the customer. Furtherto this example, the user validates one or more fields of the fullprofile associated with the customer, and provides supplemental customerinformation to refine/modify one or more fields of the full profileassociated with the customer. In this example, prediction module 310employs the validated/modified fields to more accurately determine theremaining missing fields of the full profile associated with thecustomer.

In some embodiments, prediction module 310 is further configured toevaluate the predictive modeling technique being applied to generatesaid estimated customer information. Examples of techniques employed toevaluate the performance of the predictive models include mean squarederror, root mean squared error, median absolute deviation, receiveroperating characteristic (ROC) curve, ROC area under the curve (AUC)statistic, confusion matrix, lift scores, precision and recalltechniques and the like. For example, a computer can be configured toevaluate a performance of the predictive model based at least in part onthe user modifying the full profile via the computer 112 after theapplication of the model and to take a corrective action accordingly,such as via adjusting or modifying at least one of an input to themodel, the persona, the model, the application of the model, or anyother characteristic or aspect, such as via preset algorithms orheuristics or artificial intelligence algorithms. In these embodiments,the performance evaluation of the predictive modeling techniquesprovides insights as to the accuracy of each predictive modelingtechnique given a set of known basic customer attributes included withinthe basic profile of the customer. Further to these embodiments, saidinsights enable prediction module 310 to select and apply the predictivemodeling technique that can most accurately generate estimated customerinformation based on the set of known basic customer attributes includedwithin the basic profile of the customer. In these embodiments, theperformance evaluation of the predictive modeling techniquesadditionally provides insights that can enable prediction module 310 toprioritize the attributes included within the basic customer informationthat profile analytics engine 306 requests from the user and/or assignsoptimal weights to each attribute or variable included within the basicprofile of the customer for the prediction process, if desired. In theseembodiments prediction module 310 selects and applies a performanceevaluation technique based on the amount and type of known basiccustomer information included within the basic profile associated withthe customer and the predictive modeling technique being applied togenerate the estimated customer information.

In an example, to evaluate the predictive modeling techniques,prediction module 310 determines that there is a set of 150 basicattributes known about a customer based on the basic profile of thecustomer. Continuing the example, prediction module 310 analyzes 130basic attributes from the 150 basic attributes known about the customer,employing nearest-neighbor algorithms. Based on said analysis,prediction module 310 then generates estimated customer informationincluding those 20 basic attributes known about the customer, which havenot been taken into consideration in the prediction process. In thisexample, prediction module 310 evaluates the performance of thepredictive modeling technique by calculating the difference between thetrue values of these 20 known basic customer attributes and the valuesdetermined by prediction module 310 for those same 20 basic customerattributes. The performance evaluation of the nearest-neighbor algorithmprovides insights about how accurately the nearest-neighbor algorithmperformed given the set of 150 known basic customer attributes.

In operation, a user creates a new account for a customer and providesbasic customer information by interacting with a user interface runningon a client computing device and coupled to profile analytics engine306. Data processing module 308 operating within profile analyticsengine 306 receives and processes said basic customer information,receives and processes additional customer information from one or moreexternal databases 304, and generates a basic profile for the customerbased on said basic customer information and said additional customerinformation. Data processing module 308 then stores said basic profileat internal database 302. Prediction module 310 operating within profileanalytics engine 306 requests and receives the basic profile associatedwith the customer from internal database 302 or from data processingmodule 308. Prediction module 310 additionally receives a plurality ofpersonas from internal database 302. Prediction module 310 then analysessaid basic profile by applying predictive modeling techniques, employingsaid personas as training data to generate estimated customerinformation. Prediction module 310 then pre-populates the one or moremissing fields of the full profile associated with the customeremploying said estimated customer information. Prediction module 310then stores said full profile at internal database 302 and profileanalytics engine 306 then displays said full profile to the user viaclient computing devices. The user can then validate the determinedcustomer information included within said full profile and providesupplemental customer information to data processing module 308 torefine/modify one or more fields of the full profile associated with thecustomer, if desired. Prediction module 310 then evaluates theperformance of the predictive modeling technique applied to generate theestimated customer information.

FIG. 4 is a block diagram illustrating another sub-system of aninsurance needs system pertaining to an insurance needs engine,according to an embodiment. In FIG. 4, sub-system 400 includes internaldatabase 402 and insurance needs engine 408. Insurance needs engine 408further includes insurance recommendation module 410. It should beunderstood that sub-system 400 can include less components, morecomponents, or different components depending on the desired analysisgoals and that the components may be arranged differently thanillustrated in sub-system 400 of FIG. 4.

In FIG. 4, insurance needs engine 408 is operatively coupled to and inbidirectional communication with internal database 402 throughcommunication networks (not shown in FIG. 4). In an example andreferring to FIG. 1, insurance needs engine 408 functions in asubstantially similar manner to insurance needs engine 108 withininsurance needs system 100. Continuing the example, internal database402 functions in a substantially similar manner to internal database 102within insurance needs system 100 of FIG. 1.

In some embodiments, each software module within insurance needs engine408 includes a separate processing unit for running algorithms orcomputer-executable program instructions related to the operation of themethods described in FIGS. 5 and 6. The processing unit includes aprocessor with computer-readable medium, such as, for example a randomaccess memory (RAM) coupled to the processor (not shown in FIG. 4).Examples of processor types include a microprocessor, an applicationspecific integrated circuit (ASIC), and a field programmable objectarray (FPOA), among others.

In some embodiments, insurance needs engine 408 is configured togenerate one or more insurance products for recommendation to a customerbased on a full profile associated with the customer, to provide saidinsurance product recommendation to a user interface for displaying to auser associated with the customer, receive additional insurance-needsdata from the user via the user interface, and to modify and to provideto said customer the insurance product recommendation associated withthe customer based on the received insurance-needs data and/oradditional insurance-needs data, and the full profile associated withthe customer.

In some embodiments, insurance recommendation module 410 is a softwaremodule including insurance/financial tools, data mining and other dataprocessing, configured to receive the full profile associated with acustomer from internal database 402, generate one or more recommendedinsurance products for the customer based on said full profile, andprovide said insurance-product recommendation to internal database 402and/or one or more users. In these embodiments, insurance recommendationmodule 410 receives the full profile associated with the customer frominternal database 402 and analyzes said full profile employingfinancial/insurance tools (e.g., risk and/or underwriting analysis),data mining and other data processing to identify the insurance needsfor the customer based on the current financial situation of thecustomer, demographic information, health information, and one or moreproposed future goals of the customer. In these embodiments, based onsaid insurance needs, insurance recommendation module 410 generates oneor more prioritized insurance product recommendations to build financialsecurity that enable the customer to protect his/her welfare, as well asfulfilling one or more proposed future goals (e.g., new house, new car,investments, new additions to the family, education), among others. Inother embodiments, insurance recommendation module 410 generates theinsurance-product recommendation based on the full profile of thecustomer, insurance-needs data provided by insurance needs engine 408,and the additional insurance-needs data provided by the user. In someembodiments, insurance recommendation module 410 stores the insuranceproduct recommendation at internal database 402.

In some embodiments, insurance recommendation module 410 receivesadditional insurance-needs data from a user associated with the customerto modify the insurance-products recommendation previously identified byinsurance recommendation module 410. In these embodiments, insurancerecommendation module 410 processes said additional insurance-needs dataand then generates the insurance product recommendation for the customerbased on the full profile associated with the customer and saidadditional insurance-needs data provided by the user.

In some embodiments, insurance needs engine 408 displays theinsurance-product recommendation to the one or more users interactingwith a user interface running on a client computing device and coupledto insurance needs engine 408. In these embodiments, theinsurance-product recommendation information includes instructionsdirected towards implementing a proper insurance plan/product for thecustomer while accomplishing one or more future goals (e.g., new house,new car, investments, new additions to the family, education), amongothers. In an example, an individual wishes to get married and have achild within the next year. Continuing this example, insurancerecommendation module 410 analyzes the full profile associated with thecustomer, identifies the insurance needs of the customer and recommendsan insurance product that can allow a customer to protect the welfareand future goals of his/her person and/or family.

In operation, insurance recommendation module 410 operating withininsurance needs engine 408 requests and receives the full profileassociated with a customer from internal database 402. Insurancerecommendation module 410 then analyzes said full profile to identifyone or more insurance needs of the customer employingfinancial/insurance tools, data mining and other data processing.Insurance recommendation module 410 then generates the insurance productrecommendation that best fits the customer needs based on said insuranceneeds information. Insurance recommendation module 410 then stores saidinsurance-product recommendation at internal database 402 and insuranceneeds engine 408 displays said insurance-product recommendation to theuser via client computing devices. The user can then provide additionalinsurance-needs data to insurance recommendation module 410 torefine/modify the insurance-product recommendation of the customer. Inan example, insurance recommendation module 410 operating withininsurance needs engine 408 can recommend an insurance product to acustomer based on attributes, such as, for example gender, maritalstatus, children/dependents, geographic location, financial records,etc. In this example, a full profile is generated based on the providedattributes and the full profile is passed to insurance recommendationmodule 410, insurance recommendation module 410 then analyzes the fullprofile associated with the customer to identify the insurance needs andrecommend an insurance product that best fits the needs of theindividual, as well as, his/her future goals. In some embodiments,insurance recommendation module 410 can generate a modified insuranceproduct recommendation based on the provided additional insurance-needsdata from the customer as well as the full profile associated with thecustomer.

Process Flow Diagrams for Assessing Insurance Needs Using PredictiveModeling Techniques

FIG. 5 is a flow diagram describing a method for assessing insuranceneeds using predictive analytics, according to an embodiment. In FIG. 5,method 500 begins when a profile analytics engine operating within aninsurance needs system generates a basic profile for a customer based onbasic customer information received from the user and/or additionalcustomer information received from external databases. Further to thisembodiment, the profile analytics engine generates estimated customerinformation based on said basic customer information and said additionalcustomer information using predictive modeling techniques. The profileanalytics engine then pre-populates one or more missing fields of thefull profile associated with the customer based on said estimatedcustomer information. In this embodiment, an insurance needs engineoperating within the insurance needs system, analyzes the full profileassociated with the customer to identify the insurance needs of thecustomer and then generates an insurance product(s) for the customer.

In FIG. 5, method 500 includes a plurality of steps that can beperformed by one or more computing devices, such as, for examplecomputing device 200 described in FIG. 2, implementing/running one ormore modules/processes of the exemplary operating environments of FIGS.1-4. The steps of method 500 are embodied in a computer-readable mediumcontaining computer-readable code such that the steps are implementedwhen the computer-readable code is executed by a computing device. Insome implementations, certain steps of method 500 can be combined,performed simultaneously, or in a different order, without deviatingfrom the objective of method 500.

At step 502, a profile analytics engine within the insurance needssystem generates a basic profile for a customer. In some embodiments, auser interacts with a user interface running on a client computingdevice and coupled to a profile analytics engine to create a customeraccount and provide basic customer information. In these embodiments, adata processing module operating within the profile analytics enginereceives and processes said basic customer information and generates abasic profile associated with the customer, which the data processingmodule then stores at an internal database. In an example and referringto FIGS. 1 and 3, a user creates a customer account and provides basiccustomer information, by interacting with the user interface running onone or more the client computing devices and coupled to the profileanalytics engine. In this example, the data processing module receivesand processes said basic customer information, generates a basic profilefor the customer based on the received basic customer information, andstores said basic profile at the internal database.

At step 504, the profile analytics engine requests additional customerinformation from one or more external databases. In some embodiments,the data processing module employs the basic customer informationreceived from the user at step 502 to request additional customerinformation from one or more external databases. In these embodiments,the data processing module receives and processes said additionalcustomer information and updates the basic profile associated with thecustomer stored within the internal database. In other embodiments, thedata processing module provides the updated basic profile of thecustomer to a prediction module for further analysis. In an example andreferring to FIGS. 1 and 3, the data processing module sends a requestfor additional customer information to the external database (e.g.,Acxiom of Little Rock, Ark. USA). Said request can include basiccustomer information in the form of attributes provided by the user,such as, for example, name, age, gender, ethnicity, place of residence,marital status, and others. In this example, the data processing modulereceives additional customer information from the external databaseincluding 200 or more attributes associated with the customer, where thedata processing module can employ to update the basic profile of thecustomer stored at the internal database. At step 506, the profileanalytics engine runs a predictive modeling technique to generateestimated customer information. In some embodiments, the estimatedcustomer information is an estimate of the insurance needs of thecustomer and other customer attributes. In these embodiments, aprediction module operating within the profile analytics engine receivesthe basic profile associated with the customer from the internaldatabase or from the data processing module. Further to theseembodiments, the prediction module additionally receives a plurality ofpersonas from the internal database where said personas includes aninitial set of previously generated personas, as well as, non-personallyidentifiable information derived from the full profile of customersstored within the internal database. In these embodiments, theprediction module analyzes the basic profile associated with thecustomer by applying a predictive modeling technique employing saidpersonas as training data to generate the estimated customerinformation. In some embodiments, the prediction module is able todetermine and apply the predictive modeling technique that can moreaccurately determine the estimated customer information based on thetype and number of attributes included within the basic customerinformation received from the user at step 502 and the additionalcustomer information received from the external databases at step 504.In an example, the prediction module receives the basic profileassociated with the customer from the internal database or from the dataprocessing module. In this example, the prediction module additionallyreceives a plurality of personas from the internal database. Continuingthis example, the prediction module analyzes the basic profileassociated with the customer by applying a predictive modeling techniqueemploying the personas as training data to determine the estimatedcustomer information.

In another example, the prediction module can apply K-nearest-neighboralgorithms (K-NNA) to generate estimated customer information. In thisexample, the prediction module receives the basic profile associatedwith the customer from the internal database or from the data processingmodule. The prediction module additionally receives a plurality ofpersonas from the internal database. Continuing the example, theprediction module trains the K-NNA employing said personas as trainingdata. Further to this example, the prediction module then identifiesK-nearest-neighbors, based on a similarity measure computed between eachpersona and the customer. Examples of similarity measures that theprediction module can employ include the Euclidean distance, PearsonCorrelation Coefficient, Manhattan distance, a custom similaritymeasure, and the like. In this example, the prediction module determinesthe K-nearest-neighbors of the customer by choosing the K personas thatscore the lowest on the distance test or the highest on the similitudetest depending on the similarity measure employed. The value of K refersto the number of nearest-neighbors or personas that best match the basicprofile associated with the customer, where said value of K is apositive integer, typically small, and that can be a user-defined valueor a value optimized by the K-NNA based on the training data and on thebasic information known about the customer. Continuing this example, theprediction module employs conditional probability distributions todetermine the most likely value to populate each missing field withinthe full profile based on the basic profile associated with the customerand the information regarding the previously identifiedK-nearest-neighbors or K closest personas to the customer.

Further to the example, the prediction module can generate estimatedcustomer information, employing the K-NNA by applying the Euclideandistance metric for measuring the similarity between the personas andthe customer. In this example, the prediction module analyzes the basicprofile associated with the customer, and trains the K-NNA employing thepersonas as training data. The training phase of the K-NNA involves theprediction module generating an n-dimensional feature vectorrepresenting each training example or persona and mapping these featurevectors in Euclidean space. The prediction module additionally generatesand maps a feature vector representing the basic profile associated withthe customer. Continuing the example, the prediction module computes theEuclidean distance between the feature vectors representing the basicprofile associated with the customer and the feature vector representingeach persona. The prediction module then selects K-nearest-neighbors orK personas whose Euclidean distance is the smallest, where K is apositive integer optimized by the K-NNA. In this example, the predictionmodule employing K-NNA determines that the best value of K is 10, basedon the training data and the basic profile associate with the customer.If the customer is a 40-year-old male from Washington, then the 10nearest-neighbors of the customer are individuals who can also be male,who can be approximately the same age and can be approximately from thesame geographical area. Continuing the example, the prediction moduleemploys conditional probability distributions to determine the mostlikely value with which to populate each missing field within the fullprofile associated with the customer based on the basic profileassociated with the customer and the information about the previouslyidentified 10 nearest-neighbors or 10 closest personas. In this example,the prediction module determines the missing value for rent in the fullprofile associated with the customer by computing a conditionalprobability distribution of the amount of dollars per month spent onrent. The prediction module computes said conditional probabilitydistribution using the values of amount of dollars per month spent onrent by the 10 nearest-neighbors or 10 closest personas to the customer.The prediction module then chooses the value that has the highestprobability of occurrence based on the conditional probabilitydistribution previously calculated.

In yet another example, the prediction module applies non-negativematrix factorization to determine the estimated customer information. Inthis example, the prediction module generates an n×m matrix X, where theelements of said matrix X are the values of the attributes or variablesincluded within the basic profile associated with the customer and thepersonas stored within the internal database. Matrix X includes missingmatrix elements corresponding to the missing fields within the fullprofile associated with the customer that will be determined. Continuingthe example, the prediction module applies matrix decompositionalgorithms to matrix X, therefore generating an n×r matrix W and an r×mmatrix H, which are two non-negative matrices such that matrix X=WH+U;where U is a residual error and r is a value smaller than the value of nand the value of m. In this example, the value of r is determined by theprediction module. The prediction module selects the number of columnsof matrix W and the number of rows of matrix H, so that their product WHwill approximate matrix X taking into consideration that a residualerror U remains. Further to this example, the prediction moduledetermines the missing values in matrix W and matrix H where said valuesare selected in order to reduce the value of the residual error Uemploying optimization algorithms. Optimization algorithms that can beemployed to determine the missing values of matrix W and matrix Hinclude Lee and Seung's multiplicative update rule, non-negative leastsquares, the projected gradient descent methods, the active set method,and the block principal pivoting method, among others. After the missingvalues in matrix W and matrix H are determined by the prediction moduleto reduce the value of U, the prediction module then calculates matrix Xby computing the product of matrix W and matrix H to obtain the finalvalues of the missing fields within the full profile associated with thecustomer.

In some embodiments, the greater the number of attributes the userprovides within the basic customer information at step 502 to generatethe basic profile associated with the customer, the more accurately thesystem can determine the estimated customer information employingpredictive modeling techniques.

At step 508, the profile analytics engine pre-populates one or morefields of the full profile associated with the customer based on theestimated customer information determined at step 506. In someembodiments, the prediction module employs the estimated customerinformation determined at step 506 to pre-populate one or more missingfields within the full profile associated with the customer and then theprediction module stores the full profile at the internal database. Inan example and referring to FIGS. 1 and 3, the prediction modulepre-populates one or more missing fields of the full profile associatedwith the customer based on the estimated customer information, and thenstores said full profile at the internal database. Method 500 thenadvances to step 510.

At step 510, the profile analytics engine displays the full profile tothe user for validation purposes. In some embodiments, the userinterface, which is running on a client computing device and coupled tothe profile analytics engine displays the full profile to the user. Inthese embodiments, the full profile is associated with the customer andhas been pre-populated at step 508 employing estimated customerinformation obtained at step 506. Further to these embodiments, the usercan review the customer information included within the full profile andassociated with the customer, to validate and/or refine/modify theaccuracy of the estimated customer information predicted at step 506. Inan example, the user interface running on the client computing devicesand displays the full profile associated with the customer.

At step 512, the validity of the customer information contained withinthe full profile is determined. In some embodiments, the user validatesthe customer information included within the full profile associatedwith the customer and/or determines if supplemental customer informationis required to modify one or more customer attributes or variablesincluded within the full profile associated with the customer. In someembodiments, if the user determines that supplemental customerinformation is required to modify one or more customer attributes orvariables included within the full profile associated with the customer,method 500 advances to step 514. In these embodiments, if the uservalidates the customer information included within the full profile,method 500 advances to step 516.

At step 514, supplemental customer information to refine/modify one ormore of the pre-populated fields associated with the full profile isreceived from the user. In some embodiments, the user interacts with theuser interface running on a client computing device and coupled to theprofile analytics engine to provide said supplemental customerinformation. In these embodiments, said supplemental customerinformation adjusts/modifies one or more fields of the full profilepreviously pre-populated at step 508. Further to these embodiments, theuser can additionally validate one or more fields of the full profilepreviously pre-populated at step 508. In these embodiments, the dataprocessing module receives and processes the supplemental customerinformation provided by the user to validate and/or modify one or morepre-populated fields of the full profile associated with the customer,and updates the basic profile associated with the customer stored withinthe internal database. In an example, the user interacts with the userinterface running on one or more of the client computing devices toprovide supplemental customer information. In this example, the useradditionally validates one or more previously pre-populated fields ofthe full profile, and the data processing module processes saidsupplemental customer information and adjusts/modifies one or morepre-populated fields of said full profile. In this example, the dataprocessing module updates the basic profile associated with the customeremploying said supplemental customer information and stores the updatedbasic profile at the internal database. Method 500 then advances to step506 to re-analyze the basic profile associated with the customer usingpredictive modeling techniques at least one more time to more accuratelydetermine the remaining missing fields of the full profile associatedwith the customer.

At step 516, an insurance needs engine operating within an insuranceneeds system generates an insurance-product recommendation based on thefull profile associated with the customer. In some embodiments, aninsurance recommendation module operating within the insurance needsengine receives the full profile associated with the customer from theinternal database. In these embodiments, the insurance recommendationmodule analyzes the full profile associated with the customer employingfinancial/insurance tools (e.g., risk and/or underwriting analysis) datamining and other data processing to identify the insurance needs of thecustomer and generates an insurance-product recommendation that bestfits the customer needs, as well as future goals of the customer.Further to these embodiments, the insurance recommendation module storesthe insurance product(s) information at the internal database. In otherembodiments, the insurance recommendation module displays the insuranceproduct(s) to one or more users via a client computing device. In anexample, the insurance recommendation module receives the full profileassociated with the customer from the internal database. In thisexample, the insurance recommendation module analyzes the full profileassociated with the customer employing insurance tools, data mining andother data processing to identify the insurance needs of the customerand generate an insurance-product recommendation that best fit thecustomer needs, as well as, future goals of the customer. Further tothis example, the insurance recommendation module stores theinsurance-product recommendation at the internal database. Still furtherto this example, the insurance recommendation module displays theinsurance product(s) to a user via the client computing devices. Theprocess for recommending an insurance product(s) for a customer isfurther described in FIG. 6, below.

By executing method 500 using the exemplary operating environmentsdescribed in FIGS. 1-4, big data analytics, predictive models and otherinsurance tools can be implemented for a more efficient and fasterprocessing of larger data sets. Big data analytics allow insuranceinstitutions or insurance companies to profile customers in a morefar-reaching manner than possible with human-intervention data-drivenanalysis. In this way, efficiencies are created by providing means toreduce the level of data-entry efforts required from the user togenerate one or more insurance product for a customer, as compared toconventional processes employing established methodology. These featuresallow performing large work such as time consuming analysis, data-entrytasks, filling customer profiles and generating insurance products, in amore efficient manner than other approaches including manual workperformed by humans. In some embodiments, method 500 can be performedunder 30 minutes, 25 minutes, 20 minutes, 15 minutes, 10 minutes, 5minutes, or 1 minute. Note that other time periods can be included, suchas intermediary to any of the foregoing.

FIG. 6 is a flow diagram describing a method for recommending aninsurance product for a customer, according to an embodiment. In someembodiments and referring to FIG. 5, method 600 describes the operationsperformed at step 516 of method 500. In FIG. 6, method 600 begins whenan insurance recommendation module operating within an insurance needsengine is called to analyze the full profile associated with a customerthat is received from an internal database. In some embodiments, theanalysis seeks to identify the insurance needs of the customer andfurther generate one or more insurance product recommendations that bestfit the needs of the customer, as well as, one or more defined futuregoals.

In FIG. 6, method 600 includes a plurality of steps that can beperformed by one or more computing devices, such as, for example,computing device 200 described in FIG. 2 implementing/running one ormore modules/processes of the exemplary operating environments of FIGS.1-4. The steps of method 600 are embodied in one or morecomputer-readable medium containing computer-readable code such that thesteps are implemented when the computer-readable code is executed by acomputing device. In some implementations, certain steps of method 600can be combined, performed simultaneously, or in a different order,without deviating from the objective of method 600.

At step 602, an insurance needs engine within an insurance needs systemanalyzes the full profile associated with a customer to identify theinsurance needs of the customer. In some embodiments, an insurancerecommendation module operating within the insurance needs enginereceives the full profile associated with a customer from an internaldatabase. In these embodiments, the insurance recommendation moduleemploys insurance tools (e.g., risk and/or underwriting analysis) datamining and other data processing to analyze the full profile associatedwith the customer, identify the insurance needs of the customer, anddetermine whether said insurance product(s) could meet the insuranceneeds of the customer and/or fulfill the future goals of the customer.In an example, the insurance recommendation module operating within theinsurance needs engine receives the full profile associated with acustomer from the internal database. In this example, the insurancerecommendation module employs data mining and other data processing andinsurance tools to analyze the full profile of the customer, identifythe insurance needs of the customer, and determines whether saidinsurance product(s) could meet the insurance needs of the customerand/or the future goals of the customer. In another example andreferring to FIG. 4, the insurance recommendation module determineswhich insurance product(s) (e.g., health insurance, whole lifeinsurance, term life insurance, universal life insurance, variableuniversal life (VUL), disability income insurance, annuities, long termcare, and the like) can meet the insurance needs associated with thecustomer based on the analysis of the full profile.

In yet another example and referring to FIG. 4, insurance recommendationmodule 410 determines one or more insurance needs that should beconsidered when generating the insurance product recommendation for thecustomer. In this example, the insurance needs of the customer can bedirected towards identifying a proper insurance plan/product for thecustomer, maintaining the welfare of the customer and/or family, as wellas accomplishing one or more future goals (e.g., retire within the next3 years), among others. In an example, the insurance recommendationmodule 410 can determine and recommend to a customer an insuranceproduct based on the full profile that can include attributes, such as,for example married, 3 children, 34 years old, civil engineer, living inArizona, as well as, proposed future goals, such as, for example, investin a own business, save for retirement, move to another city/state, geta house/car, etc.

At step 604, the insurance needs engine generates and stores aninsurance-product recommendation for the customer. In some embodiments,the insurance recommendation module generates an insurance-productrecommendation for the customer based on the insurance needs identifiedat step 604, and stores said insurance-product recommendation at theinternal database. In an example, the insurance recommendation modulegenerates an insurance product recommendation for the customer based onthe insurance needs identified at step 602. In this example, saidinsurance needs can be directed towards acquiring one or more insuranceproducts, such as, for example health insurance, whole life insurance,term life insurance, universal life insurance, variable universal life(VUL), disability income insurance, annuities, long term care, and thelike. Continuing the example, said insurance product recommendationprovides customers with insurance products that allow the customers tofulfill one or more proposed future goals. In another example andreferring to FIGS. 1 and 4, the insurance product recommendationincludes the insurance product(s) which allow a customer to reduce riskand increase protection based on the associated full profile of thecustomer, provide cost effective protection based on the associated fullprofile of the customer, as well as provide benefits informationassociated with the insurance products contained within the insuranceproduct recommendation.

In an example, said insurance-product recommendation allows a customerto reduce his/her risk, thus enabling the customer to cover anyunexpected future events, such as, death, illness, and disability. Inthis example, insurance recommendation module 410 stores theinsurance-product recommendation of the customer at the internaldatabase.

At step 606, the insurance needs engine displays the insurance-productrecommendation to one or more users. In some embodiments, the userinterface running on a client computing device and coupled to theinsurance needs engine displays the previously generatedinsurance-product recommendation to one or more users. In an example,the user interface running on the client computing devices and coupledto the insurance needs engine displays the insurance-productrecommendation previously generated at step 604 to one or more users. Inthis example, a user can be an insurance agent or a customer who wantsto obtain one or more insurance product(s) that best fit the needs ofthe customer.

In other embodiments, the user can provide additional insurance-needsdata to the insurance needs engine via client computing devices tomodify the insurance-needs data previously identified by the insurancerecommendation module at step 602. In these embodiments, the insurancerecommendation module processes said additional insurance-needs data andthen generates and displays an insurance-product recommendation thatbest fit the insurance needs and/or future goals of the customer basedon said additional insurance-needs data and the full profile associatedwith the customer. Thus, if the user desires to modify the previouslyidentified insurance needs, then method 600 advances to step 602 toestablish a new set of insurance needs and generates a new/modifiedinsurance-product recommendation for the customer. Alternatively, if theuser desires to validate and/or modify one or more fields of the fullprofile associated with the customer, then method 600 advances to step514 of method 500 of FIG. 5, to validate and/or modify one or morepre-populated fields of the full profile associated with the customer.

FIG. 7 is a flow diagram describing a method for evaluating a predictivemodeling technique, according to an embodiment. In an embodiment, method700 seeks to identify true and false values for user attributes. Inanother embodiment, the true and false values may be used in order toupdate a machine learning or cognitive aspect of the present disclosure.For example, the prediction module may use the false values or theprediction evaluation to update a neural network or a similar nodalstructure in order to improve future predictions. In someimplementations, certain steps of method 700 can be combined, performedsimultaneously, or in a different order, without deviating from theobjective of method 700.

At step 702, the prediction module determines whether there is a set ofbasic attributes known about a customer based on the basic profile ofthe customer. Customer attributes may, in one embodiment, be received bythe user interacting with the user interface of one or more of theclient computing devices and analyzed as described in FIG. 5.

At step 704, the prediction module analyzes a portion of the basicattributes from the basic attributes known about the customer. Theprediction module may employ, in an embodiment, a nearest neighboralgorithm as described in FIG. 5. In an example, the prediction modulemay identify 150 customer attributes and only analyze 130.

At step 706, the prediction module generates estimated customerinformation for the customer attributes not considered by the predictivemodeling. For example, data including those 20 basic attributes knownabout the customer which have not been considered in the predictionprocess.

At step 708, the prediction module calculates the difference between thetrue values of the known basic customer attributes, such as the 20 knownbasic customer attributes.

At step 710, the prediction module evaluates the performance of thepredictive modeling technique by evaluating the difference between thetrue values and predictive values of the customer attributes. Theevaluation, in one embodiment, revolves around the logic that the moresimilar the two values, the better the predictive modeling technique. Inan embodiment, this evaluation may be quantified and, depending onwhether the evaluation satisfies a pre-determined threshold, thepredictive modeling technique may be used for similar customers.

The foregoing method descriptions and the interface configuration areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe steps in the foregoing embodiments may be performed in any order.Words such as “then,” “next,” etc. are not intended to limit the orderof the steps; these words are simply used to guide the reader throughthe description of the methods. Although process flow diagrams maydescribe the operations as a sequential process, many of the operationscan be performed in parallel or concurrently. In addition, the order ofthe operations may be re-arranged. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination may correspond to a return ofthe function to the calling function or the main function.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedhere may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middle-ware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anymeans including memory sharing, message passing, token passing, networktransmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description here.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed here may be embodied in a processor-executable software modulewhich may reside on a computer-readable or processor-readable storagemedium. A non-transitory computer-readable or processor-readable mediaincludes both computer storage media and tangible storage media thatfacilitate transfer of a computer program from one place to another. Anon-transitory processor-readable storage media may be any availablemedia that may be accessed by a computer. By way of example, and notlimitation, such non-transitory processor-readable media may compriseRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other tangible storagemedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computeror processor. Disk and disc, as used here, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or instructions on anon-transitory processor-readable medium and/or computer-readablemedium, which may be incorporated into a computer program product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedhere may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown here but is to beaccorded the widest scope consistent with the following claims and theprinciples and novel features disclosed here.

What is claimed is:
 1. A method comprising: receiving, by a server froma computing device, a request to generate a recommendation associatedwith a first user, the server further receiving a set of attributes ofthe first user from a graphical user interface displayed on thecomputing device; identifying, by the server, at least one missingattribute associated with the first user; identifying, by the server, anexisting user profile corresponding to a second user having the set ofattributes; executing, by the server, an artificial intelligence modeltrained based on personas corresponding to a set of existing users,wherein the artificial intelligence model uses the set of attributes ofthe identified existing user profile to estimate the at least onemissing attribute of the first user; updating, by the server, a userprofile of the first user using the estimated at least one missingattribute generated by the artificial intelligence model; generating, bythe server, the recommendation based on the updated user profile; andtransmitting, by the server to the computing device, the recommendationto be displayed on the graphical user interface.
 2. The method of claim1, wherein the artificial intelligence model uses personas correspondingto non-personally identifiable data of existing users.
 3. The method ofclaim 1, further comprising displaying, by the server, the estimated atleast one missing attribute on the graphical user interface.
 4. Themethod of claim 3, wherein the server generates the recommendation inresponse to receiving an approval associated with the at least onemissing attribute from the computing device.
 5. The method of claim 4,wherein when the server receives an input from the computing devicecorresponding to a denial of the estimated at least one missingattribute, the server re-executes the artificial intelligence model torevise the estimated at least one missing attribute.
 6. The method ofclaim 1, wherein the graphical user interface is displayed in a browserapplication.
 7. The method of claim 1, wherein the recommendation is aninsurance product.
 8. The method of claim 1, wherein the server assignsa priority weight to each attribute within the set of attributes.
 9. Themethod of claim 1, wherein the artificial intelligence model prioritizesone or more attributes based on their respective priority weight whenestimating the missing attribute.
 10. The method of claim 1, wherein theartificial intelligence model uses a K-nearest-neighbor algorithm or anon-negative matrix factorization algorithm to calculate the at leastone missing attribute.
 11. A computer system comprising: a computingdevice configured to display a graphical user interface having aplurality of input fields configured to receive a set of attributes of afirst users; and a server in communication with the computing device,the server configured to: receive, from the computing device, a requestto generate a recommendation associated with the first user, the serverfurther receiving a set of attributes of the first user from thegraphical user interface displayed on the computing device; identify atleast one missing attribute associated with the first user; identify anexisting user profile corresponding to a second user having the set ofattributes; execute an artificial intelligence model trained based onpersonas corresponding to a set of existing users, wherein theartificial intelligence model uses the set of attributes of theidentified existing user profile to estimate the at least one missingattribute of the first user; update a user profile of the first userusing the estimated at least one missing attribute generated by theartificial intelligence model; generate the recommendation based on theupdated user profile; and transmit, to the computing device, therecommendation to be displayed on the graphical user interface.
 12. Thesystem of claim 11, wherein the artificial intelligence model usespersonas corresponding to non-personally identifiable data of existingusers.
 13. The system of claim 11, further comprising displaying, by theserver, the estimated at least one missing attribute on the graphicaluser interface.
 14. The system of claim 13, wherein the server generatesthe recommendation in response to receiving an approval associated withthe at least one missing attribute from the computing device.
 15. Thesystem of claim 14, wherein when the server receives an input from thecomputing device corresponding to a denial of the estimated at least onemissing attribute, the server re-executes the artificial intelligencemodel to revise the estimated at least one missing attribute.
 16. Thesystem of claim 11, wherein the graphical user interface is displayed ina browser application.
 17. The system of claim 11, wherein therecommendation is an insurance product.
 18. The system of claim 11,wherein the server assigns a priority weight to each attribute withinthe set of attributes.
 19. The system of claim 11, wherein theartificial intelligence model prioritizes one or more attributes basedon their respective priority weight when estimating the missingattribute.
 20. The system of claim 11, wherein the artificialintelligence model uses a K-nearest-neighbor algorithm or a non-negativematrix factorization algorithm to calculate the at least one missingattribute.